Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models
Paper in proceeding, 2023
Goal: to compare the accuracy of three defect backlog prediction methods in the context of large open-source (OSS) projects, i.e., ARIMA, Exponential Smoothing (ETS), and the state-of-the-art method developed at Ericsson AB (MS).
Method: we perform a simulation study on a sample of 20 open-source projects to compare the prediction accuracy of the methods. Also, we use the Naïve prediction method as a baseline for sanity check. We use statistical inference tests and effect size coefficients to compare the prediction errors. Results: ARIMA, ETS, and MS were more accurate than the Naïve method. Also, the prediction errors were statistically lower for ETS than for MS (however, the effect size was negligible).
Conclusions: ETS seems slightly more accurate than MS when predicting defect backlog size of OSS projects.
Author
Paulina Aniola Sielicka
Sushant Kumar Pandey
University of Gothenburg
Software Engineering 1
Miroslaw Staron
Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)
University of Gothenburg
Miroslaw ochodek
Poznan University of Technology
Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023
83-92
978-83-967447-8-4 (ISBN)
Warsaw, Poland,
Subject Categories (SSIF 2025)
Software Engineering
Computer Sciences
DOI
10.15439/2023F5474